class_docstr: Base class for model based tuner
  This type of tuner will fit a cost model and use an optimizer to
  find the maximums of the cost model as next trials
  
  Parameters
  ----------
  task: autotvm.task.Task
      The tuning task
  cost_model: CostModel
      The cost model that predicts the speed of a config (IR)
  model_optimizer:
      The optimizer to find local optimum points of cost model in tuning search space
  plan_size: int
      Tuner will re-fit model per `plan_size` new measure samples
  diversity_filter_ratio: int or float, optional
      If is not None, the tuner will first select
      top-(plan_size * diversity_filter_ratio) candidates according to the cost model
      and then pick plan_size of them according to the diversity metric.
code: def has_next(self):
      return len(self.visited) < len(self.space)
  
imports: import gc
  import numpy
  from .tuner import Tuner
  from ..env import GLOBAL_SCOPE
label_desc: Use image node_id 5 for calling the ModelBasedTuner obj's underlying member method code with example usage: obj.has_next() and returns: unknown